The evaluation of graph-based optimization vs. filter-based approaches for structure from motion has been rigorously analyzed in [1]}. The conclusion is that optimization approaches outperform filtering in terms of accuracy per unit of computing time. Visual SLAM improves accuracy by increasing
the number of features while having minor effects when increasing the number of keyframes. However, the analysis was done with a standard Extended Kalman filter (EKF) formulation, which models the features in the vector state, and the computational time grows quadratically with the number of observations. Other approaches such as the Multi-State Constrained Kalman Filter (MSCKF) [2]} express the constraints without including the 3D feature position in the filter state vector, resulting in computational complexity linear in the number of features. The work in [3]} showed that MSCKF competes with graph-based optimization approaches in terms of accuracy and computational cost. We focus our method on a filter-based approach using the MSCKF formulation of the JPL xVIO library, targeting onboard rotorcraft flight applications. We extend the xVIO back-end to fuse multi-agent data, developing a collaborative formulation of the MSCKF. We use the Covariance Intersection (CI) algorithm [4]} in order to avoid the filter being overconfident in the resulting updates. The CI overestimates the resulting covariance after fusing the measurements coming from several agents, ensuring filter consistency. Our back-end is similar to the research in [5]} and [6]}.
